Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions.

BACKGROUND: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE: The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS: In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS: We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS: This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.

Transmission cluster characteristics of global, regional, and lineage-specific SARS-CoV-2 phylogenies.

The SARS-CoV-2 pandemic has been presenting in periodic waves and multiple variants, of which some dominated over time with increased transmissibility. SARS-CoV-2 is still adapting in the human population, thus it is crucial to understand its evolutionary patterns and dynamics ahead of time. In this work, we analyzed transmission clusters and topology of SARS-CoV-2 phylogenies at the global, regional (North America) and clade-specific (Delta and Omicron) epidemic scales. We used the Nextstrain's nCov open global all-time phylogeny (September 2022, 2,698 strains, 2,243 for North America, 499 for Delta21A, and 543 for Omicron20M), with Nextstrain's clade annotation and Pango lineages. Transmission clusters were identified using Phylopart, DYNAMITE, and several tree imbalance measures were calculated, including staircase-ness, Sackin and Colless index. We found that the phylogenetic clustering profiles of the global epidemic have highest diversification at a distance threshold of 3% (divergence of 10, where the tree sampled median is 49). Phylopart and DYNAMITE clusters moderately-to-highly agree with the Pango nomenclature and the Nextstrain's clade. At the regional and clade-specific scale, transmission clustering profiles tend to flatten and similar clusters are found at distance thresholds between 0.05% and 25%. All the considered phylogenies exhibit high tree imbalance with respect to what expected in random phylogenies, suggesting short infection times and antigenic drift, perhaps due to progressive transition from innate to adaptive immunity in the population.

A scoping review of semantic integration of health data and information.

OBJECTIVE: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS: The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION: SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Severe Acute Respiratory Syndrome Coronavirus 2 Delta Vaccine Breakthrough Transmissibility in Alachua County, Florida.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant has caused a dramatic resurgence in infections in the United Sates, raising questions regarding potential transmissibility among vaccinated individuals. METHODS: Between October 2020 and July 2021, we sequenced 4439 SARS-CoV-2 full genomes, 23% of all known infections in Alachua County, Florida, including 109 vaccine breakthrough cases. Univariate and multivariate regression analyses were conducted to evaluate associations between viral RNA burden and patient characteristics. Contact tracing and phylogenetic analysis were used to investigate direct transmissions involving vaccinated individuals. RESULTS: The majority of breakthrough sequences with lineage assignment were classified as Delta variants (74.6%) and occurred, on average, about 3 months (104 ±â€…57.5 days) after full vaccination, at the same time (June-July 2021) of Delta variant exponential spread within the county. Six Delta variant transmission pairs between fully vaccinated individuals were identified through contact tracing, 3 of which were confirmed by phylogenetic analysis. Delta breakthroughs exhibited broad viral RNA copy number values during acute infection (interquartile range, 1.2-8.64 Log copies/mL), on average 38% lower than matched unvaccinated patients (3.29-10.81 Log copies/mL, P < .00001). Nevertheless, 49% to 50% of all breakthroughs, and 56% to 60% of Delta-infected breakthroughs exhibited viral RNA levels above the transmissibility threshold (4 Log copies/mL) irrespective of time after vaccination. CONCLUSIONS: Delta infection transmissibility and general viral RNA quantification patterns in vaccinated individuals suggest limited levels of sterilizing immunity that need to be considered by public health policies. In particular, ongoing evaluation of vaccine boosters should specifically address whether extra vaccine doses curb breakthrough contribution to epidemic spread.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review.

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. MATERIALS AND METHODS: We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. RESULTS: In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. DISCUSSION: Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. CONCLUSION: There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

Correction: A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis.

[This corrects the article DOI: 10.2196/19170.].

A snapshot of SARS-CoV-2 genome availability up to April 2020 and its implications

BACKGROUND: : The SARS-CoV-2 pandemic has been growing exponentially, affecting over four million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published in scientific journals, as well as through non-peer reviewed channels, to investigate SARS-CoV-2 genetic heterogeneity and spatiotemporal dissemination. Yet, a systematic investigation of phylogenetic information and sampling bias in the available data is missing. OBJECTIVE: The objective of this study was to determine the quality of the current SARS-CoV-2 full genome data, in terms of sampling bias as well as phylogenetic and temporal signal, to inform and guide the scientific community. METHODS: We used maximum likelihood based methods to assess the presence of sufficient information for robust phylogenetic and phylogeographic studies in several SARS-CoV-2 sequence alignments, assembled from GISAID data released between March and April 2020. RESULTS: Although number of high quality full-genomes is growing daily, and recent sequence data released in April contains sufficient phylogenetic information that would allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 data sets still present severe limitations. CONCLUSIONS: At the present time, studies assessing within country spread or transmission clusters should be considered preliminary at best, or hypothesis generating. Hence, the need for interpreting current reports with caution, and continuing concerted efforts to increase number and quality of sequences required for robust tracing of the epidemic. CLINICALTRIAL: INTERNATIONAL REGISTERED REPORT: RR2-https://doi.org/10.1101/2020.04.01.020594.

The global spread of 2019-nCoV: a molecular evolutionary analysis.

The global spread of the 2019-nCoV is continuing and is fast moving, as indicated by the WHO raising the risk assessment to high. In this article, we provide a preliminary phylodynamic and phylogeographic analysis of this new virus. A Maximum Clade Credibility tree has been built using the 29 available whole genome sequences of 2019-nCoV and two whole genome sequences that are highly similar sequences from Bat SARS-like Coronavirus available in GeneBank. We are able to clarify the mechanism of transmission among the countries which have provided the 2019-nCoV sequence isolates from their patients. The Bayesian phylogeographic reconstruction shows that the 2019-2020 nCoV most probably originated from the Bat SARS-like Coronavirus circulating in the Rhinolophus bat family. In agreement with epidemiological observations, the most likely geographic origin of the new outbreak was the city of Wuhan, China, where 2019-nCoV time of the most recent common ancestor emerged, according to molecular clock analysis, around November 25th, 2019. These results, together with previously recorded epidemics, suggest a recurring pattern of periodical epizootic outbreaks due to Betacoronavirus. Moreover, our study describes the same population genetic dynamic underlying the SARS 2003 epidemic, and suggests the urgent need for the development of effective molecular surveillance strategies of Betacoronavirus among animals and Rhinolophus of the bat family.